Social Media Mining
Behavior Analytics
Examples of Behavior Analytics
• What motivates individuals to join an online
group?
• When individuals abandon social media sites,
where do they migrate to?
• Can we predict box office revenues for movies
from tweets posted by individuals?
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• To answers these questions we need to analyze or
predict behaviors on social media.
• Individuals exhibit different behaviors in social
media: as individuals
• or as part of a broader collective behavior.
• When discussing individual behavior, our focus is on
one individual.
• Collective behavior emerges when a population of
individuals behave in a similar way with or without
coordination or planning.
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Our Goal
To analyze, model, and predict individual and
collective behavior
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Individual Behavior
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Types of Individual Behavior
• User-User
(link generation)
befriending, sending a
message, playing games,
following, inviting
• User-Community:
joining or leaving a
community, participating
in community discussions
• User-Entity
(content generation)
writing a post, posting a
photo
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I. Individual Behavior
Analysis
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Community Membership in Social Media
• Why do users join communities?
– What factors affect the community-joining behavior of individuals?
• We can observe users who join communities and determine
the factors that are common among them
• We require a population of users, a community C, and
community membership information (i.e., users who are
members of C).
– Communities can be implicit: One can think of individuals buying a
product as a community, and people buying the product for the first
time as individuals joining the community.
– To distinguish between users who have already joined the
community and those who are now joining it, we need community
memberships at two different times t1 and t2, with t2> t1.
– At t2, we determine users such as u who are currently members of
the community, but were not members at t1. These new users form
the subpopulation that is analyzed for community-joining behavior.
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Community Membership in Social Media
• Hypothesis: individuals are
inclined toward an activity
when their friends are engaged
in the same activity.
• A factor that plays a role in
users joining a community is
the number of their friends
who are already members of
the community.
• In data mining terms, this
translates to using the number
of friends of an individual in a
community as a feature to
predict whether the individual
joins the community (i.e., class
attribute).
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Even More Features
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Feature Importance Analysis
• which feature can help best determine whether
individuals will join or not?
• We can use any feature selection algorithm.
Feature selection algorithms OR
• We can use a classification algorithm, such as
decision tree learning
– Most important Features are Ranked Higher
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Decision Tree for Joining a Community
• Are these features well-designed?
– We can evaluate using classification performance
metrics.
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Behavior Analysis Methodology
• An observable behavior: the behavior needs to
be observable. E.g., accurately observing the joining
of individuals (and possibly their joining times).
• Features: design relevant data features (covariates)
that may or may not affect (or be affected by) the
behavior – [we need a domain expert for this step]
• Feature-Behavior Association: Find the
relationship between features and behavior. E.g., use
decision Tree
• Evaluation: the findings are due to the features and
not to externalities. E.g., use classification accuracy
– We can also use randomization tests
– Or causality testing algorithms
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Granger Causality
• Consider a linear regression model
– We can predict Yt+1 by using either Y1, Y2 … Yt or a
combination of X1, X2 … Xt and Y1, Y2 … Yt.
– If
then X Granger Causes Y
• (why is this not causality)
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II. Individual Behavior
Modeling
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Individual Behavior Modeling
• Models in Economics, Game Theory, and Network
Science
• We can use:
– Threshold Models: We need to learn thresholds and weights
– Then, Wij can be defined as the
• fraction of times user i buys a product and user j buys the same product
soon after that
• When is soon?
– Similarly, thresholds can be estimated by taking into account the
average number of friends who need to buy a product before user
i decides to buy it.
– What if no friends / or friends don’t buy the same products?
Then we can find the most similar individuals or items (similar
to collaborative filtering methods)
– Cacade Models
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III. Individual Behavior
Prediction
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Individual Behavior Prediction
• Most behaviors result in newly formed links in
• social media.
– It can be a link to a user, as in befriending behavior;
– A link to an entity, as in buying behavior; or
– A link to a community, as in joining behavior.
• We can formulate many of these behaviors as a
link prediction problem.
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Link Prediction
• Most behaviors result in newly formed links in
• social media.
– It can be a link to a user, as in befriending behavior;
– A link to an entity, as in buying behavior; or
– A link to a community, as in joining behavior.
• We can formulate many of these behaviors as a
link prediction problem.
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Link Prediction - Setup
• Link prediction assumes a graph G(V; E).
• Let e(u,v) represent an interaction (edge) between nodes u and v,
and let t(e) denote the time of the interaction.
• Let G[t1,t2] represent the subgraph of G such that all edges are
created between t1 and t2
– i.e., for all edges e in this subgraph, t1 < t(e) < t2).
• Now given four time stamps t11 < t12 < t21 < t22 a link prediction
algorithm is given the subgraph G[t11,t12] (training interval) and is
expected to predict edges in G[t21,t22] (testing interval).
• We can only predict edges for nodes that are present during the
training period
• Let G(Vtrain; Etrain) be our training graph. Then, a link prediction
algorithm generates a sorted list of most probable edges in
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Link Prediction - Algorithms
• Assign
to every edge e(x,y)
• Edges sorted by this value in decreasing order
will create our ranked list of predictions
• Note that any similarity measure between two
nodes can be used for link prediction; therefore,
methods discussed in Chapter 3 are of practical
use here.
• We will review some well-known methods
– Node Neighborhood-Based Methods
– Path-Based Methods
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Computing Link Scores
• Common Neighbors: the more common
neighbors that two nodes share, the more similar
they are
• Jaccard Similarity: the likelihood of a node
that is a neighbor of either x or y to be a
common neighbor.
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More Measures
• Adamic-Adar:if two individuals share a
neighbor and that neighbor is a rare neighbor, it
should have a higher impact on their similarity.
• Preferential Attachment: nodes of higher
degree have a higher chance of getting connected
to incoming nodes
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Example
• Compute the edge score for edge (5,7)
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Path-Based Measures
• Katz Measure:
•
denotes the number of paths of length l
between x and y. is a constant that
exponentially damps longer paths.
– When small the measure reduces to common
neighbor measure
– It can be reformulated in Closed Form as
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Path-Based Measures
• Hitting Time and Commute Time: Consider
a random walk that starts at node x and moves to
adjacent nodes uniformly. Hitting time is the
expected number of random walk steps needed
to reach y starting from x.
– a smaller hitting time implies a higher similarity;
therefore, a negation can turn it into a similarity
measure
– If y is highly connected random walks are
more likely to visit y
• We can normalize it using the stationary probability
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• Hitting time is not symmetric, we can use
commute time instead or its normalized version
• Some other examples
– Rooted PageRank: the stationary probability of y,
when at each random walk run you can jump to x with
probability P and to a random node with probability
1-P
– SimRank: Recursive definition of similarity
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Link Prediction - Evaluation
• After one of the aforementioned measures is selected, a list of
the top most similar pairs of nodes are selected.
• These pairs of nodes denote edges predicted to be the most
likely to soon appear in the network.
• Performance (precision, recall, or accuracy) can be evaluated
using the testing graph and by comparing the number of the
testing graph’s edges that the link prediction algorithm
successfully reveals.
• Performance is usually very low, since many edges are created
due to reasons not solely available in a social network graph.
• So, a common baseline is to compare the performance with
random edge predictors and report the factor improvements
over random prediction.
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Collective Behavior
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Collective Behavior
• First Defined by sociologist Robert Park
• Collective Behavior: A group of individuals
behaving in a similar way
• It can be planned and coordinated, but often is
spontaneous and unplanned
• Examples:
• Individuals standing in line for a new product release
• Posting messages online to support a cause or to show
support for an individual
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I. Collective Behavior
Analysis
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Collective Behavior Analysis
• We can analyze collective behavior by analyzing
individuals performing the behavior
• We can then put together the results of these
analyses
• The result would be the expected behavior for a
large population
• OR, we can analyze the population as a whole
• Not very popular for analysis, as individuals are
ignored
• Popular for Prediction purposes
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Collective Behavior Analysis
• We can analyze collective behavior by analyzing
individuals performing the behavior
• We can then put together the results of these
analyses
• The result would be the expected behavior for a
large population
• OR, we can analyze the population as a whole
• Not very popular for analysis, as individuals are
ignored
• Popular for Prediction purposes
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Collective Behavior Analysis - Example
• Users migrate in social media due to their limited time and
resources
• Sites are often interested in keeping their users, because they
are valuable assets that help contribute to their growth and
generate revenue by increasing traffic
• Two types of migrations:
• Site migration: For any user who is a member of two
sites s1 and s2 at time ti, and is only a member of s2 at time
tj > ti, then the user is said to have migrated from site s1 to
site s2.
• Attention Migration: For any user who is a member of
two sites s1 and s2 and is active at both at time ti, if the
user becomes inactive on s1 and remains active on s2 at
time tj > ti, then the user’s attention is said to have
migrated away from site s1 and toward site s2.
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Collective Behavior Analysis - Example
• Activity (or inactivity) of a user can be determined by
observing the user’s actions performed on the site.
• We can consider a user active in interval [t, t+X ], if the
user has performed at least one action on the site during
this interval. Otherwise, the user is considered inactive.
• The interval could be measured at different granularity,
such as days,
• weeks, months, and years.
– It is common to set = 1 month.
• We can analyze migrations of individuals and then
measure the rate at which the population of these
individuals is migrating across sites.
– We can use the methodology for individual behavior analysis
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The Observable Behavior
• Sites migrating is
rarely observed, while
attention migration is
clearly observable.
• We need to take
multiple steps to
observe it:
• Users are required to
be identified on
multiple networks
• Huan.liu1 on Facebook
is Liuhuan on Twitter
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Features
• User Activity: more active users are less likely to
migrate
• e.g., number of tweets, posts, photos
• User Network Size: a user with more social ties
(i.e., friends) in a social network is less likely to
move
• e.g., number of friends
• User Rank: A user with high status in a network is
less likely to move to a new one where he or she
must spend more time getting established.
• e.g., centrality scores
• External rank: your citations, how many have referred
to your article, ...
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Feature-Behavior Association
• Given two snapshots of a network, we know if users
migrated or not.
• Let vector
indicate whether any of our n users
have migrated or not.
• Let
be the features collected (activity,
friends, rank) for any one of these users at time stamp t.
• The correlation between features X and labels Y can be
computed via logistic regression.
• How can we verify that this correlation is not random?
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Evaluation Strategy
• To verify if the correlation between features and
the migration behavior is not random
• we can construct a random set of migrating users
and compute X_Random and Y_Random for them
as well
• Find the correlation between these random
variables (e.g., regression coefficients) and it should
be significantly different from what we obtained
using real-world observations
• We can use Chi-square test for significance testing
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II. Collective Behavior
Modeling
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Collective Behavior Modeling
• Collective behavior can be conveniently modeled
using some of the techniques discussed in
Chapter 4, “Network Models”.
• We want models that can mimic characteristics
observable in the population.
• In network models, node properties rarely play a
• role; therefore, they are reasonable for modeling
collective behavior.
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III. Collective Behavior
Prediction
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Collective Behavior Prediction
• From previous chapters, we could use
• Linear Influence Model (LIM)
• Epidemic Models
• Collective behavior can be analyzed either in terms of individuals
performing the collective behavior or based on the population
• as a whole.
• It is more common to consider the population as a whole
• when predicting collective behavior, we are interested in predicting
the intensity of a phenomenon, which is due to the collective
behavior of the population (e.g., how many of them will vote?)
• we can utilize a data mining approach where features that describe
the population well are used to predict a response variable (i.e., the
intensity of the phenomenon).
• A training-testing framework or correlation analysis is used to
determine the generalization and the accuracy of the predictions.
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Predicting Box Office Revenue for Movies
1.
Set the target variable that is being predicted. In this case, it
is the revenue that a movie produces.
1.
2.
3.
4.
The revenue is the direct result of the collective behavior of going
to the theater to watch the movie.
Determine the features in the population that may affect the
target variable.
Predict the target variable using a supervised learning
approach, utilizing the features determined in step 2.
Measure performance using supervised learning evaluation.
• the average hourly number of tweets related to the movie for
each of the seven days
• prior to the movie opening (seven features) and the number of
opening theaters for the movie (one feature).
• The predictions using this approach are closer to reality
than that of the Hollywood Stock Exchange (HSX), which
is the gold standard for predicting revenues for movies
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Generalizing the Idea
• Target variable y
• Some feature A that quantifies the attention
• Some feature P that quantifies the publicity
• Train a regression model
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